EfficientPolypSeg: Efficient Polyp Segmentation in colonoscopy images using EfficientNet-B5 with dilated blocks and attention mechanisms

Background: Colorectal cancer ranks as the second leading cause of cancer-related death worldwide, prompting the need for effective strategies to combat this disease. The urgent need for effective strategies to combat this disease has led to the exploration of innovative approaches in medical imaging and diagnosis. Automatic segmentation and detection of colorectal polyps in colonoscopy images have emerged as a promising approach, enabling timely identification and diagnosis of colorectal diseases. Methods: This paper introduces EfficientPolypSeg, an advanced segmentation architecture that leverages the capabilities of EfficientNet-B5 as the encoder, enhanced by two dilated blocks, a Channel Attention Block (CAB), a Spatial Attention Block (SAB), and a Feature Fusion Block (FFB). Notably, our model exhibits superior performance compared to state-of-the-art methods, achieving exceptional accuracy and segmentation quality. Results: To validate the effectiveness of EfficientPolypSeg, extensive experiments were conducted on four publicly available datasets. The results demonstrate the remarkable performance of our model, showcasing its ability to outperform existing approaches in terms of accuracy and segmentation quality. Our model achieved an impressive mean Dice score of 0.92 and a mean Intersection over Union (IoU) of 0.86 on these datasets. These findings highlight the potential impact of EfficientPolypSeg in improving the detection and diagnosis of colorectal polyps. Conclusion: EfficientPolypSeg, the segmentation architecture proposed in this study, exhibits remarkable capabilities in the automatic detection and segmentation of colorectal polyps. The model’s superior performance, as evidenced by extensive experimentation on diverse datasets, emphasizes its potential impact on improving the detection and diagnosis of colorectal diseases.